Adaptive group paging for a communication network

ABSTRACT

When traffic arrives from the network for an idle mobile device, the network executes device activation procedures to awaken the device, which can result in a significant amount of signaling to complete. Adaptive device activation mechanisms are provided that adapt to network conditions and potentially to machine-to-machine device application requests to realize scalable device activation without increasing the resources used for this purpose and without negatively impacting existing human-to-human or human-to-machine traffic.

TECHNICAL FIELD

The present application relates generally to providing adaptive groupsizes, group membership, and/or backoff parameters to enable efficientand scalable techniques for paging devices of a communication network.

BACKGROUND

Mobile devices and other user equipment that are not actively sending orreceiving data traffic enter an idle state after a brief period ofinactivity. When traffic arrives from the network for the user equipmentdevice, the network executes device activation procedures to wake thedevice up. Device activation procedures represent a significant portionof all communication network signaling.

The number of subscriber devices that connect to wireless networks hasbeen growing at a very fast pace for many years. In the past, consumerpurchase of manually-operated devices such as smart phones, tablets,etc. has driven the majority of the growth. However, a recent trend hasstarted to emerge in which a different class of device is beginning todrive a significant proportion of the growth of subscriber devices thatutilize wireless network services. Machine-to-machine devices such assmart meters, smart appliances, sensors, or the like, are not manuallyoperated by a user when connecting to the network and communicating databut are increasingly becoming subscribers of wireless networks. It isexpected that in the coming years M2M devices that use wireless networkservices will grow to the billions, possibly many hundreds of billions,which can place unsustainable stress on communication networks,specifically with regard to device activation signaling.

BRIEF DESCRIPTION OF THE DRAWINGS

Numerous aspects, embodiments, objects and advantages of the presentinvention will be apparent upon consideration of the following detaileddescription, taken in conjunction with the accompanying drawings, inwhich like reference characters refer to like parts throughout, and inwhich:

FIG. 1 illustrates a block diagram of an example communication networkthat is depicted illustrating certain issues associated with grouppaging in order to contemporaneously wake the group of devices from anidle state in accordance with certain embodiments of this disclosure;

FIG. 2 illustrates a block diagram of an example system that can providefor adaptively selecting group size data and group membership data inaccordance with certain embodiments of this disclosure;

FIG. 3 illustrates a block diagram of an example system that can providefor adaptively selecting a suitable backoff parameter associated withdelaying initiation of a network connection attempt in response to agroup paging message in accordance with certain embodiments of thisdisclosure;

FIG. 4 illustrates a block diagram of an example system that illustratesan example LTE/EPC Architecture in accordance with certain embodimentsof this disclosure;

FIGS. 5A-5C provide example graphical illustrations that depict failurerates, wait times, collisions and other data in connection with aspecific use-case in accordance with certain embodiments of thisdisclosure;

FIG. 6 illustrates a block diagram of an example system that canrepresent another example LTE/EPC demonstrating operation of disclosedadaptive mechanisms or techniques in accordance with certain embodimentsof this disclosure;

FIG. 7 depicts an example diagram that provides an example of a groupidentifier that can be used in connection with adaptive techniques inaccordance with certain embodiments of this disclosure;

FIGS. 8A-8C depict example graphical illustrations that compare RACHfailure rates, preamble transmission attempts and other data for thedisclosed adaptive techniques compared to naïve implementations inaccordance with certain embodiments of this disclosure;

FIGS. 9A-9C provide example graphical illustrations that depict RACHfailure rates, paging failure rates, and other data for the disclosedadaptive techniques compared to naïve implementations in accordance withcertain embodiments of this disclosure;

FIG. 10A depicts an example graphical illustration that provides acomparison of RACH failure rates with and without disclosed adaptionmechanisms in accordance with certain embodiments of this disclosure;

FIG. 10B depicts an example graphical illustration that provides acomparison relative to adaptive chunk sizes in accordance with certainembodiments of this disclosure;

FIG. 11 illustrates an example methodology that can provide foradaptively determining a paging group size in accordance with certainembodiments of this disclosure;

FIG. 12 illustrates an example methodology that can provide foradditional elements or aspects in connection with adaptively determininga paging group size in accordance with certain embodiments of thisdisclosure;

FIG. 13 illustrates a first example of a wireless communicationsenvironment with associated components that can be operable to executecertain embodiments of this disclosure;

FIG. 14 illustrates a second example of a wireless communicationsenvironment with associated components that can be operable to executecertain embodiments of this disclosure; and

FIG. 15 illustrates an example block diagram of a computer operable toexecute certain embodiments of this disclosure.

DETAILED DESCRIPTION

Overview

To preserve network resources and energy, user equipment (UE) devicesthat are not actively communicating enter an idle state after a shortperiod of inactivity. When devices enter this idle state, network statedata associated with the device, specifically across the radio accessnetwork (RAN), is released. Device activation then refers to themechanisms involved in “waking the device up” from this idle state andrestoring the network state used to enable the device to communicate.

Device activation by itself is not a new concept but has beenpart-and-parcel of cellular networks since their inception, and amountsto almost 30% of the total signaling traffic in many currentcommunication network deployments. Current device activation mechanismswere developed to support a single service, namely human-to-human (H2H)voice communication. Growth of data traffic from smart phone use causedthis initial use to be replaced by the current predominanthuman-to-machine (H2M) communication as users use their cellular devicesto access Internet services. Future growth is expected to bepredominantly machine-to-machine (M2M) communication, where smartdevices function without direct human mediation, and M2M communicationis rapidly becoming commonplace. The scale and unique communicationrequests or requirements of M2M pose new challenges to wide areawireless communication infrastructure like cellular networks that havebeen engineered and optimized for human initiated communication. Theexpected growth of M2M communication in future cellular networks,however, suggests that existing device activation mechanisms will beinadequate.

The device activation workloads associated with H2H and H2Mcommunication patterns are different from the workload that might beexpected in an M2M environment. For example, a server contactingthousands of smart meters every hour on the hour will present aradically different step function in terms of offered load to the deviceactivation mechanisms. More importantly, M2M communication is expectedto dominate cellular network communication presenting a scalabilityproblem that current mechanisms are simply not able to cope with. Arecent M2M study suggests that the percentage of connected M2M deviceswill grow from 23% in 2012 to 61% in 2022 and that the number of M2Mconnections is expected to grow at an annual rate of 22% from 2 billionin 2012 to 18 billion in 2022. These numbers suggest that the onslaughtof M2M devices and traffic are poised to overwhelm existing cellularnetwork mechanisms, thereby negatively impacting the experience ofmobile users at a time when the importance of cellular networkscontinues its unabated growth.

Given that current device activation mechanisms are ill suited tosupport the expected growth of M2M devices and traffic, the disclosedsubject matter can mitigate various device activation issues, whether inconnection with M2M growth or current conditions. In this regard,proposed is an adaptive device activation architecture for communicationnetworks (e.g., LTE/EPC cellular networks) that adapts to networkconditions and M2M or other application requests or requirements inorder to realize scalable device activation without increasing theresources used for this purpose. This adaptive approach can enable thenetwork to handle M2M applications with a large number of deviceswithout negatively impacting existing human-to-human (H2H) andhuman-to-machine (H2M) traffic.

Adaptive paging mechanisms (e.g., to facilitate device activation)disclosed herein can rely on fundamental group paging mechanisms thatare supported by third generation partnership project (3GPP) standardsas well as other standards bodies. Hence, paging messages that aredelivered to a device to wake that device from an idle state can beconfigured to wake an entire group of devices with a single pagingmessage. Group paging therefore reduces signaling with respect to somenetwork resources (e.g., a paging channel) since waking up devices as agroup requests or requires fewer paging messages. On the other hand,since the entire group wakes and attempts to attach to the network atsubstantially the same time, group paging can also stress other networkresources (e.g., a random access channel). The disclosed adaptive pagingmechanisms are directed to optimizing and/or balancing the trade-offsassociated with group paging that exist due to fixed resources.

The adaptive paging mechanisms disclosed herein are directed to threeprimary focus areas: mechanisms to adaptively determine an optimal oradvantageous group size, mechanisms to adaptively determine groupmembership, and mechanisms to adaptively delay random access channel(RACH) procedures that attempt to attach the device to the network(e.g., wake the device from an idle state) in order to avoid RACHcollisions while minimizing or reducing RACH procedure completion times.

Example Adaptive Group Mechanisms

The disclosed subject matter is now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the disclosed subject matter. It may beevident, however, that the disclosed subject matter may be practicedwithout these specific details. In other instances, well-knownstructures and devices are shown in block diagram form in order tofacilitate describing the disclosed subject matter.

Referring now to the drawing, with reference initially to FIG. 1,communication network 100 is depicted illustrating certain issuesassociated with group paging in order to wake the group of devices froman idle state. In some embodiments, communication network 100 can be along-term evolution (LTE) and/or evolved packet core (EPC) cellularnetwork. Network 100 can include various core network devices 102 suchas, for example, mobility management entity (MME) 104 as well as variousother core network devices not shown here.

Mobility management entities (e.g., MME 104) represent a control-nodefor the LTE access-network. MMEs are responsible for idle mode userequipment (UE) paging and tagging procedure including retransmissions.MMEs are typically involved with bearer activation/deactivationprocesses and are also generally responsible for choosing a servinggateway for a UE at initial attach and at times of intra-LTE handoverinvolving core network node relocation. MME can also be responsible forauthenticating the user (e.g., by interacting with an HSS). Non AccessStratum (NAS) signaling typically terminates at the MME, which can alsobe responsible for generation and allocation of temporary identitiesassociated with UEs. MMEs also generally provide control planefunctionality for mobility between LTE and 2GPP/3GPP access networks viaan S3 interface terminating at the MME.

In addition, network 100 can include various radio access network (RAN)devices 106 such as, for instance, access point 108 as well as one ormore UE 110. In some embodiments, access point 108 can represent anevolved Node B (eNB), hence access point 108 and eNB 108 can be usedherein interchangeably. UE(s) 110 can represent one or more H2H or H2Mdevices (e.g., cellular phones, smart phones, tablets, etc.) or M2Mdevices (e.g., smart meters, smart appliances, sensors, embeddedautomotive devices/sensors, etc.) that attach to access point 108 inorder to use services provided by network 100.

Network 100 can include a control plane 112. Control planes (e.g.,control plane 112) typically handle routing and priority aspectsassociated with network traffic and can be distinguished from a dataplane (not shown), which handle communication of the data. In otherwords, the data plane conveys network traffic, whereas the control planeprovides various functions relating to how that traffic should beconveyed as well as numerous other control functions. For example, acontrol plane (e.g., control plane 112) can exists as part of routerarchitecture that is concerned with drawing a network map or managinginformation and protocols associated with a routing table that defineshow to handle incoming packets.

At the RAN portion of the network, control plane 112 resources caninclude paging channel resources 114 and random access channel (RACH)resources 116, which are typically both constrained by fixed resourceallocation. For example, paging channel 114 generally has an upper limiton how many paging messages can be conveyed in a given period. Likewise,RACH 116 generally has an upper limit on how many devices can beawakened and/or activated within a given period. In the context of usinggroup paging, that is, waking up large groups of devices with a singlepaging message, selecting small group sizes means more paging messagesare implicated than with larger group sizes. Given that paging channel114 resources tend to be fixed, attempting to wake a large number ofdevices will request or require many paging messages, which in turn cancause an overload condition, a delay in waking up the devices, oranother failure condition with respect to paging channel 114.

On the other hand, selecting large group sizes can wake a set of deviceswith fewer paging messages and thus reduce the load on paging channel114. However, a resultant trade-off is that because many devices wakeand attempt RACH procedures contemporaneously, collisions or otherfailure conditions are much more likely on RACH 116. Further, acondition may arise in which devices are unnecessarily awakened simplyby virtue of being in a group. Thus, selecting a suitable group size canbe very advantageous for group device activation. A static group sizeselection cannot scale or adapt to changing network conditions and istherefore not viewed as a viable solution to paging issues, particularlywith regard to the estimated growth of M2M devices that will be placingadditional demands on existing infrastructure.

Advantageously, the disclosed subject matter can mitigate theaforementioned issues by adaptively selecting group sizes based onnetwork load, which is further detailed with reference to FIG. 2. Insome embodiments, additional benefits can be realized by adaptivelyselecting group membership, which is also discussed in connection withFIG. 2. Once a suitable group size is selected, the disclosed subjectmatter can determine suitable backoff parameters based on the selectedgroup size and potentially on other criteria as well. This backoffparameter can represent a period of delay before devices being awakenedas a group begin respective RACH procedures to re-establish networkconnectivity from the idle state. Mechanisms directed to backoffparameters are further detailed with reference to FIG. 3. It isunderstood that the mechanisms detailed herein can be continuallyupdated based on feedback information. Thus, after a few iterations,both paging channel 114 and RACH 116 can be substantially optimized andcontinuously adapt to changing network conditions through the processes.It is understood that control plane 112 can include many additionalresources not shown here and such resources can be monitored inconnection with determining network load data.

Turning now to FIG. 2, system 200 is provided. System 200 can providefor adaptively selecting group size data and group membership data. Insome embodiments, system 200 can provide for adaptively selecting groupmembership data and/or determining which user equipment devices (e.g.,UEs 110) are to be grouped. Generally, system 200 can comprise aprocessor and a memory that stores executable instructions that, whenexecuted by the processor, facilitate performance of operations.Examples of the memory and processor can be found with reference to FIG.15. It is to be appreciated that some portions of computer 1502 canrepresent a portion of a server device of a communications network or aportion of a user equipment device and can be used in connection withimplementing one or more of the systems or components shown anddescribed in connection with FIG. 1 and other figures disclosed herein.

System 200 can include one or more core network devices 102. As onesuitable example used for the remainder of this disclosure, this corenetwork device(s) 102 can be MME 104 that, in many communicationnetworks is responsible for tracking mobility of UEs 110 and so forth.System 200 can further include one or more access point devices 202. Asone suitable example used for the remainder of this disclosure, at leastone of these access point device(s) 202 can be eNB 108.

MME 104 can determine network load data 204 that can representutilization of a network resource associated with communication network100. In some embodiments, this network resource can be control plane 112resource (e.g., a control channel). Thus, network load data 204 can be acontrol plane load. In other embodiments, network load data 204 canrelate to other aspects of communication network 100, including in somecases a load associated with data plane resources, which can bedetermined by other network components and forwarded to MME 104. Networkload data 204 can reflect a number of devices connected to communicationnetwork 100 or to a portion of communication network 100 (e.g., aparticular serving gateway, a particular eNB 108 or other access pointdevice, etc.), a measurement of network traffic at some portion ofcommunication network 100, a determined quality of service (QoS)associated with some portion of network 100, a count or estimate ofdevices that are trying to connect to network 100, or any other suitableload metric.

MME 104 can be configured to generate a paging message data structure206 that can include various data or other data structures. For example,paging message data structure 206 can include activation data 208 andgroup size data 210 and/or a data structure that can be populated withgroup size data 210. Activation data 208 can include identifiersassociated with a group of UEs 110 to wake as well as an instruction towake said UEs 110. As used herein, the terms “wake,” “awaken,” and“activate” are used substantially interchangeably and relate toinstructing a device (e.g., UE 110) to switch from an idle state to anactive state. Generally, in order to switch to an active state from theidle state, the device (e.g., UE 110) initiates a RACH procedure toattach to communication network 100 via a selected eNB 108.

Group size data 210 can be indicative of a size of a group of UEs 110that are to be awakened as a group. Advantageously, MME 104 candetermine group size data 210 as a function of network load 204. Hence,group size data 210 can adapt based on network conditions rather thanrepresenting a static quantity. Upon determination of group size data210, such data can be stored to paging message data structure 206 andprovided to eNB 108, instructing eNB 108 to wake all the devicesincluded in the group identified by paging message data structure 206.It is understood that paging message data structure 206 can be deliveredto multiple eNB 108 devices. Since the devices of a group to be awakenedare in the idle state, the location of devices with mobility is notknown with certainty, so tracking area lists can be checked to selectthe eNB 108 devices that are most likely to serve the UEs 110 in thegroup to be awakened. For devices in which mobility is not expected(e.g., smart meters or certain other M2M devices), the location may beknown with relative certainty and/or the tracking area list mightinclude only a single (or a few) eNBs or other access point devices. Itis further understood that a group can in fact be a subgroup (e.g.,subset of the group), which is further detailed infra.

In some embodiments, MME 104 can determine group membership data 212.Group membership data 212 can be indicative of the specific UEs 110 thatare to be grouped together and thus awakened substantially together.Group membership data 212 can be determined based on various definedcharacteristic data 214 associated with potential members of the group.In some embodiments, characteristic data 214 can be, e.g., type data 216indicative of a type of UE 110 device, type size data 218 indicative ofa number of UE 110 devices of a given type in the group, frequency data220 indicative of a known or estimated frequency the UE 110 device ispaged in a specified period, last known location and/or tracking arealist data, or other suitable characteristics.

For example, selecting group membership 212 can be a function of:whether a particular UE 110 is a M2M device, or associated with aparticular network or third-party entity (e.g., type data 216); whethernumerous other UE 110 devices are already in the group (e.g., type sizedata 218); whether paging messages can be scheduled (e.g., frequencydata 220); whether a UE 110 device is in a service area associated withnetwork 100 or a third-party (e.g., smart meters in a specific zip codeor utility provider service area), whether a UE 110 device is at aparticular known or probable location or area, and so on. It isunderstood that other factors can be employed in connection withselecting group membership 212 and the above examples are intended to beillustrative but not limiting.

As noted previously, once group membership 212 and group size data 210are determined, paging message data structure 206 can be provided to eNB108 device(s) to attempt to awaken the UE 110 devices in the group. Inresponse to paging message data structure 206, eNB 108 device(s) canforward relevant information to the UEs 110 in the group, which willthen wake and attempt to connect to network 100. eNB 108 device(s) cantransmit connection requests 222 to MME 104. Ideally, all suchconnection requests 222 will complete successfully, but in some casesfailures will result instead, typically followed by reconnectionattempts. Based on these connection requests 222 and informationrelating to whether such was successful, failure rate data 224 can bedetermined by MME 104. For instance, failure rate data 224 can beindicative of a proportion of UE 110 devices of the group that failed toestablish a connection in response to the group paging.

Based on this feedback, MME 104 can, in some embodiments, initiate anupdate 226 to the previously determined group size data 210. If failurerate data 224 indicates a high failure rate, then it can be inferredthat too many UE 110 devices are attempting awaken during a given cycleand, thus, the group size is too large. Hence, such failure rate data224 can result in an update 226 that reduces the group size associatedwith group size data 210. Conversely, a low failure rate (e.g., zero)can indicate very few collisions and/or retries in connection with theRACH procedures, in which case it may be determined that the group sizecan be increased to reduce paging channel 114 overhead. It is understoodthat update 226 can reflect the most current data available includingboth failure rate data 224 and current network load data 204, as suchmight have changed in the interim.

Referring now to FIG. 3, system 300 is depicted. System 300 can providefor adaptively selecting suitable backoff parameters associated withdelaying initiation of a network connection attempt in response to agroup paging message. Apart from the adaptive grouping mechanismsdetailed at FIG. 2 that can be determined in the core network ofcommunication network 100, adaptive backoff parameters detailed belowcan be determined at a RAN portion of communication network 100, such asvia eNB 108. For example, eNB 108 can receive paging message datastructure 206, which can include activation data 208 (e.g., aninstruction to wake a group of UE 110 IDs) and group size data 210(e.g., a number of devices that are to awaken as a group that wasdetermined by MME 104 in the above example relating to FIG. 2). It isunderstood that group size data 210 can be included in activation data208 (e.g., a count of the UE 110 IDs can indicate group size).

eNB 108 can determine capacity data 302 that can be indicative of afirst number of user equipment device connections supported by eNB 108.For example, suppose eNB 108 can support network connections with 5,000UE 110 devices at a time. In that case, capacity data 302 can bedetermined to be 5,000. Such a determination can be based on a varietyof metrics associated with the configuration and hardware associatedwith eNB 108. Further, eNB 108 can determine RACH backoff data 304,which can be determined as a function of a ratio of group size data 210and capacity data 302. Further suppose that group size data 210indicates that group 306 has a group size of 1,000 UE 110 devices (e.g.,1,000 devices served by eNB 108 are to be awakened together). In theabove example, the capacity is approximately 5,000, whereas the groupsize is 1,000, so RACH backoff data 304 can be determined as a functionof a suitable ratio, e.g., 1,000/5,000, or a proportion of 20%. In someembodiments, capacity data 302 can be based on current load 310 and/or anumber of existing active connections (e.g., connections with devicesthat are not in group 306). For example, if 1,000 devices are activelyconnected to eNB 108 with a capacity of 5,000, then RACH backoff data304 can be determined as a function of 1,000/(5,000−1,000) or aproportion of 25%.

Upon determining RACH backoff data 304, paging message 308 can betransmitted to the group 306. Paging message 308 can include activationdata 208, instructing those idle UE 110 devices in group 306 to wake.Paging message 308 can also include RACH backoff data 304 that instructsthe UE 110 device in group 306 to delay attempting to attach to eNB 108by a random amount of time (or according to another delay mechanism).Because of this random delay, not all UE 110 devices of group 306 willattempt to wake at the same time, but rather according to a randomdistribution of start times bounded by time window 312 that is includedin RACH backoff data 304. Due in part to this delay that can beindependently determined by each UE 110, RACH collisions can be reducedsince those UE 110 device will initiate RACH procedures 314 (e.g., toattach to eNB 108) only after the random delay, the bounds of which arespecified by RACH backoff data 304. Moreover, recall that RACH backoffdata 304 can be determined based on capacity data 302. Hence, timewindow 312 can adapt in response to a current capacity associated witheNB 108.

In addition to the random delay determined based on time window 312, eNB108 can, in some embodiments, determine another delay parameter 316.Delay parameter 316 can be directed to preventing overlap between RACHprocedures 314 associated with a first paging message 308 and RACHprocedures 314 associated with a second paging message 308. Delayparameter 316 can be determined as a function of completion times 318 orcompletion rate 320 and can be included in paging message 316.

Completion times 318 relates to a time by which RACH procedures 314associated with group 306 are completed (e.g., all UE 110 devices havesuccessfully attached to eNB 108). It is observed that, ideally,completion time 318 will not be significantly more than time window 312or another suitable time, but if that is not the case, delay parameter316 can make up the difference. Completion rate 320 can relate to aproportion of UE 110 devices in group 306 that completed RACH procedures314.

In some embodiments, eNB 108 can determine update 322. Update 322 can bedetermined as a function of completion rate 320. Update 322 can beemployed to update RACH backoff data 304. For example, if completionrate 320 is below a given threshold, such indicates that too many UE 110devices failed to complete RACH procedures 314. One reason for such maybe that RACH 116 was overwhelmed and/or too many collisions occurred,which, upon retry, cascaded to many more collisions until a timeout.However, by increasing time window 312, such difficulties can bemitigated, thus RACH backoff data 304 can be adaptively updated based oncompletion rate 320, as well as based on capacity data 302 (which mayhave changed in the interim).

Example Architectures, Embodiments, and Benefits

In this section, the impact of M2M growth on the specific cellularnetwork function of device activation is explored. As will bedemonstrated, expected M2M use cases will indeed overwhelm currentdevice activation mechanisms, leading to unacceptably large activationtimes, or even failure to activate devices. Network-agnostic naivestrategies to address these concerns simply shift the overload conditionfrom one mechanism to another. Based on these insights, a holistic andadaptive approach to device activation is provided herein in one or moreexample embodiments. An architecture for cellular device activation isprovided herein that will allow a network to dynamically adapt themanner in which it performs device activation by taking into accountcurrent network conditions as well as M2M application specific dynamics.

Some advantages provided by the disclosed subject matter are moreefficient device activation without requesting or requiring additionalradio resources, minimal changes to existing mechanisms, and withoutreliance on applications to adapt their behavior to protect the network.To these ends, a focus detailed herein is to develop a detailedunderstanding of the existing device activation mechanisms in LTE accessnetworks, to identify the primary bottlenecks associated with suchmechanisms that impact the network performance and to explore the realmof adaptive techniques that the network can apply to perform efficientdevice activation even in the presence of large scale M2M arrivals.Specifically, a group-based device activation procedure is proposed,using a co-operative paging and random access scheme, that dynamicallyadapts the group sizes based on the current network conditions.

It is noted that overload control for M2M arrivals is considered a highpriority item by 3GPP standards body, and different methods for overloadcontrol are proposed including group based access of M2M devices. Thedisclosed subject matter is aligned with these efforts, and addressesmany of the issues left open by earlier proposals concerning groupassignment and management, optimal group size selection, adaptive loaddistribution, and (sub)group activation scheduling. Also, our methods donot request or require access-barring or exclusive resource reservationfor M2M to prevent network overload unlike some of the earlier proposedschemes.

One design principles is to not change the legacy H2H devices, andadaptation of the M2M device activation procedures in order to minimizethe impact on the existing H2H communications. The disclosedembodiments, thus, also support broader thinking that differentdevice/applications will be treated differentially and our groupingbased approach, while following the same adaptive methods within agroup, also adapt to different (access) methods used across groups andprovides architectures to support such differential group treatment.

Certain advantages provided by the disclosed subject matter can include,for example:

-   -   A demonstration, using data driven simulation, of the fragile        nature of current device activation mechanisms and the negative        impact that M2M applications have on existing H2H and H2M        traffic. Results show that large scale M2M arrivals can cause        unacceptably high failure rates (as high as 38.58%), and        increased device activation time (factor 4 increase) when using        the existing mechanisms.    -   An adaptive device activation architecture is presented that        allows the network to accommodate M2M applications without        overloading the network. In this regard, the disclosed        architectures can enable dynamic group formation of M2M devices        and execute group based activation mechanism to prevent network        overload conditions.    -   Group based activation algorithms are disclosed that allow the        network to dynamically adapt to network conditions and to        efficiently distribute the M2M load ensuring minimal impact to        ongoing H2H communications. For example, a co-operative paging        and random access scheme is proposed that allows efficient        distribution of M2M load based on availability of the limited        control plane resources. Also presented are an adaptive backoff        scheme that enables efficient distribution of M2M load, and an        algorithm to select the optimal paging group sizes dynamically,        based on current network conditions. Such mechanisms can also        ensures proper separation of consecutive group accesses to        achieve very low failure rates.    -   Also presented is a simulator model of the LTE device activation        procedure to evaluate the disclosed algorithms and/or mechanisms        using data from a large cellular provider as background (H2M)        traffic. The model is evaluated with a demanding M2M use case        (30000 arrivals/10s) to show the efficient scalability of our        adaptive algorithms under extreme workloads. Further, disclosed        mechanisms are equally applicable to other (not so demanding)        scenarios as they dynamically adapt to network conditions to        ensure efficient and/or optimal use of limited network        resources. Results demonstrate the disclosed adaptive mechanisms        can achieve almost zero failure rates in device activation, with        negligible impact on H2H communications, even in the presence of        very demanding M2M arrivals.

The forthcoming examples, embodiments, and/or advantages are provided inthe context of long term evolution (LTE) and evolved packet core (EPC)technology. Turning now to FIG. 4, system 400 illustrates an exampleLTE/EPC Architecture. System 400 can represent an example embodiment ofcommunication network 100 of FIG. 1. System 400 depicts variouscomponents of an LTE/EPC mobile communication system, with shadedcomponents forming part of the disclosed adaptive approach. The LTE RANconsists of eNodeBs (e.g., eNBs 108), which communicate with mobiledevices (e.g., UEs 110) via the radio link. The eNodeB also performsradio resource control and cooperates with the MME (e.g., MME 104) formobility management. The EPC consists of the MME, serving gateway(s)(S-GW), and packet data network gateway(s) (P-GW). The MME is a controlplane only function responsible for user authentication via the HSS(Home Subscriber Server) and mobility management. It also interacts withthe S-GW for data session establishment/release. The S-GW and the P-GWare on the data path, and their main function is packetrouting/forwarding.

When the UE has no data to send, it goes to an idle state (e.g., lowenergy state) with no active radio connection and the data tunnel t1 istorn down by the network. In this idle state, the location of the UE isknown to the network in the form of a tracking area (TA) list ofeNodeBs, which the MME assigns to the UE. An idle UE wakes upperiodically according to a configured discontinuous reception (DRX)cycle to check for potential activation requests (paging) from theeNodeB.

LTE/EPC device activation consists of two procedures, paging and randomaccess (RACH). If a data packet arrives from the external network for aUE in the idle state, the MME sends a paging request to all the eNodeBsin the TA list of that UE. The eNodeBs, in turn, calculate the pagingslot for the UE based on the UE identifier and send a paging indication(e.g., paging message 308) in the calculated slot. The UE wakes upperiodically (e.g., once per DRX cycle) to monitor the paging channelfor incoming paging requests in its corresponding slot. If the UE findsa paging indication in the slot and one of the UEID(s) (multiple UEIDscan map to the same paging slot) sent in the corresponding pagingmessage matches its own identifier, the UE starts connection setuptowards the network using a random access channel (RACH) procedure(e.g., RACH procedure 314). Both paging and RACH related messages aresent on a shared control channel with limited resources. The network islimited in the number of devices that can be activated per paging orrandom access slot. Hence if a large number of devices contend for theselimited resources at the same time, it will lead to excessive activationfailures and activation delays.

While still referring to FIG. 4, but turning now as well to FIGS. 5A-5C,graphic illustrations 500, 510, and 520 are depicted, respectively.Graphic illustrations 500, 510, and 520 depict failure rates, waittimes, collisions and other data in connection with a specific use-case.These figures illustrate the fragile nature of current LTE/EPC deviceactivation mechanisms by considering a demanding, but realistic, usecase involving smart meters in a dense urban environment. Specifically,the use case involves 30,000 M2M devices (smart meters) per cell, beingactivated by the network in a 10 second interval to report status, forexample because of a restored power failure or periodic reportingfrequently used in current M2M deployments. A pull-based model isassumed in this example, which is aligned with the approaches advocatedby 3GPP standards body.

A data driven simulation framework is used, which is described in detailbelow, to show the inadequacy of the current device activation procedureto handle the M2M-induced overload scenario described above (30000request arrivals over 10 s). The first column in FIG. 5A shows that forthe demanding M2M load, the existing individual paging (IP) mechanismsresult in a 38.58% paging failure. The impact on background H2H trafficis equally severe with a 30.05% paging failure. Note that, these resultsare for a network configured with very high paging capacity. Inoperational networks, the practical capacity can be much less, so evenless demanding M2M use cases can cause similar breakdown of the currentprocedures.

At the same time, a network agnostic naive grouping strategy (GP), whereall the M2M devices are paged as a single group to solve the pagingoverload problem, will overwhelm the current RACH procedure as shown inthe second column of the FIG. 5A. FIG. 5C shows how the RACH performancedeteriorates with increasing M2M group arrival sizes.

The M2M induced overload scenario also increases the device activationtime for both M2M and background H2H traffic (FIG. 5B). It is noted that3GPP target device activation time (excluding paging delay) is below 50ms. High activation times negatively impact user-perceived performance,and can also lead to timeouts in long-lived TCP connections, typicallymaintained by application servers to current smart phones.

These results provide motivation to design an adaptive group-baseddevice activation procedure that dynamically adapts the paging groupsize based on the available RACH capacity. In essence, a collaborativedesign is proposed that performs a joint optimization of the paging andthe RACH procedure, based on the available limited control planeresources, at the same time ensuring that existing H2H communicationsare minimally impacted by demanding M2M use cases. An alternate designcould be to use a single paging-group, and a sufficiently large randombackoff window in RACH to reduce contention. We do not consider thisalternate design because of its inflexibility to adapt to currentchannel condition. The available channel capacity continuously changesas devices from different applications with diverse access patternsaccess the network. A conservative selection of the backoff window todeal with the varying channel capacity will lead to inefficient channelutilization. On the other hand, an aggressive backoff window will resultin increased collisions in presence of temporal high load. Spreading M2Mpaging into batches controlled by the network gives finer control astime evolves, and allows efficient utilization of the scarce resourcesby opportunistically adapting the batch sizes based on networkconditions.

Referring now to FIG. 6, system 600 is depicted. System 600 representsanother example LTE/EPC that demonstrates operation of disclosedadaptive mechanisms or techniques. System 600 can represent anotherembodiment of communication network 100 detailed in connection withFIGS. 1-3. As discussed, various architectures and the adaptivealgorithms can be employed to enable efficient, scalable deviceactivation in the context of an LTE/EPC network architecture.Fundamental to some embodiments of the disclosed approach is the abilityfor mobile devices to be paged as a group, and dynamically adapt thegroup assignment based on the network conditions. The adaptivecomponents of such a design are described below.

System 600 depicts certain examples of key components of the design withvarious steps or acts #1-#9 labeled in FIG. 6 as numerals 1-9 in blackcircles. The design enables the network to page a large set of devicesusing batches of chunks (subgroups), and to dynamically adapt the chunksizes based on the current network condition. The adaptive groupingfunction in the MME is responsible for selection of a suitable chunk(subgroup) size, and for generation of a proper Group ID (#1) to send apaging request (#2) for the corresponding chunk to the eNB. The eNB, inturn, determines suitable backoff parameters based on the selected chunksize to ensure low collision probabilities in the RACH access (#3), andsends a paging request across the RAN (#4) for the corresponding chunk.The UEs belonging to the chunk initiate their RACH access (#5) based onthe eNB indicated backoff parameters. The eNB adjusts the RACHparameters for the subsequent chunks based on the RACH completion rateof the previous chunks (#6). Similarly, the MME adapts the current chunksize based on the connection setup request rates received from the eNB(#7). The Assign Group IDs function is responsible for an initialadaptable group identifier assignment to the UEs based on eitherexplicit group information provided by applications (#8) or byin-network learning in the MME (#9).

Paging a large set of devices as a group improves the paging channelefficiency significantly but, as a side effect, leads to dramaticincrease in RACH failures and collisions. Presented is an adaptivebackoff scheme, based on the group size, to prevent devices in the samegroup from competing for the random access channel at the same instant.It is proposed that when a group of N devices are paged using a singlegroup identifier, all the devices in the group select and wait for arandom amount of time B_(init) in [0,f(N)], before starting the RACHprocedure. f(N) is calculated as follows:f(N)=(N/N _(RA) ^(max))*T _(RA)  (1)where T_(RA) is the periodicity of random access slots as configured bythe network, and N_(RA) ^(max) denotes the maximum number of devicesthat can complete the RACH procedure in each slot. Therefore, f(N) is anestimate of the time taken for a group of N devices to complete the RACHprocedure in an ideal scenario with no background traffic and nocollisions. The intuition behind such a choice of the backoff window,[0,f(N)], is that if everything goes well, the devices will backoff forjust the right amount of time and initiate the RACH procedures such thatthere are no collisions while all RACH slots are fully utilized. The eNBadaptive RACH function 602 computes the value of f(N) and communicatesthe same to the devices in the paging message itself.

It is understood that the concept of using random backoff to distributeload is not new, and also proposed as solutions for overload control in3GPP. Thus, contribution associated with the disclosed subject matter isa simple yet efficient backoff scheme that can adapt to different M2Marrival patterns and, as demonstrated below, provides the flexibility todistribute a group arrival in smaller chunks according to networkconditions, dynamically. Below is an example algorithm to determine anon-overlap delay parameter (e.g., delay parameter 316).

Algorithm 1 Algorithm for finding δ_(a) for chunk i of size C 1: if (Atleast one of the previous chunks have completed RACH access) then 2:   

 _(C) = RACH access completion time for the most recent completed chunk3: else 4:   

 _(C) = K * f(C) (from Equation 1) 5: end if 6: numRemaining = number ofdevices of chunk (i − 1) still accessing the RACH 7: If chunk (i−1) hasnot started RACH access yet. numRemaining = C 8: δ_(a) = ( 

 _(C)/C) * numRemaining

It is understood that when using smaller chunks to activate a largenumber of devices, it is possible for the paging request for a chunk toarrive at a time when the RACH procedure for some previous chunk(s) isin progress. In such scenarios, RACH access attempts of the differentchunks will overlap with each other leading to increased RACH collisionsand failure. To prevent this, we propose that the eNB sends anadditional delay parameter δ_(a) in the paging message and all thedevices belonging to the chunk defer their RACH attempt by an additionalδ_(a) time. The eNB activation monitor function 604 monitors the RACHcompletion times (R_(C)) 318 of the previous chunks and passes theinformation to the eNB Adaptive RACH function 602, which, in turn, usesthe most recent R_(C) 318 value to dynamically estimate δ_(a) usingAlgorithm 1, for instance. R_(C) 318 can be estimated based on themeasurement of the most recent RACH access completion time of a chunk.In the case where some devices of a chunk fail to complete the accessprocedure, the eNodeB can detect such failures using existingtimeout-based methods, and determine the chunk access completion timebased on the information of both successful and failed access counts(e.g., completion rate 320).

Initially when an estimate of RC 318 is not available, the eNBconservatively sets R_(C)=K*f(C) where the factor K accounts foradditional delays related to background load, collisions etc. The factorK only impacts the first few chunks until one of the previous chunkscompletes RACH access, and can be set based on past history (K=3 is usedin this evaluation). The eNB sends f(C) and δ_(a) along with the groupidentifier in the paging message, where C is the chunk size, and alldevices belonging to a chunk first wait for δ_(a) time and then initiatea backoff for a random time in [0,f(C)] before starting the RACHprocedure.

Instead of the devices waiting for an additional δ_(a) time, the eNBcould possibly delay sending the paging message by the same amount oftime. However, this alternative may not be preferred because a pagingmessage can only be sent in the specific slots corresponding to thegroup identifiers, and waiting for the next paging slot will incurunnecessary delays (at least one paging DRX cycle) in the accesscompletion.

With regard to automatic chunk size selection, presented now is a methodthat allows the network to dynamically estimate, based on the currentcontrol channel load, a chunk size that provides low failure rates forboth paging and RACH, while minimizing the RACH access time. In thismethod, the network initially selects a default minimum chunk size(e.g., 100) that avoids high paging failures (very small chunk sizeswill result in large number of paging messages being sent out, therebyincreasing paging collision and failure rates). Then the networkdynamically adapts the chunk size based on the observed networkconditions.

According to this approach, the network selects the chunk size, C, suchthat:N _(C) ^(T) =T/d _(C) ≦Ps  (2)and,N _(C) ^(T) *R _(C) ≦T  (3)where T is the paging DRX cycle, d_(C) is the arrival duration of achunk of C devices, N_(C) ^(T) is the number of chunks that arrivewithin a paging DRX cycle, P_(s) is the paging slot capacity, and, R_(C)is the estimated RACH access delay for chunk size C. Equation 2 ensuresa maximum capacity of a paging slot is not exceeded, and Equation 3ensures that all previous chunks have completed their access procedurebefore the new set of chunks arrive in the next DRX cycle. If no C thatsatisfies equations 2 and 3 is found, a search for a suitable C isrepeated over two DRX cycles, e.g., we try to find C, such that chunksarriving over 2T can complete their RACH access in 2T without exceedingthe overall paging capacity 2*Ps. E yen after searching over two DRXcycles, if a suitable C is not found, we choose C such that d_(C)=2T,e.g., we choose a chunk size whose arrival duration is close to (butless than) 2T. This choice ensures that the average delay experienced bydevices in a chunk does not exceed the paging DRX cycle value, T. Ourselected chunk size depends on R_(C), which, in turn, depends on thenetwork condition, e.g., R_(C) is likely to increase when the network isexperiencing high load. The disclosed techniques ensure that theselected chunk size dynamically adapts to changing network conditions.

Referring now to FIG. 7, illustration 700 is presented. Illustration 700provides an example of a group identifier that can be used in connectionwith adaptive techniques disclosed herein. With regard to adaptive groupidentifiers, presented is a group assignment approach that the networkmay utilize to implement the adaptive algorithms described earlier. Thenetwork assigns the devices a group identifier (ID) which is explicitlycommunicated by the network using control message exchanges e.g., duringinitial attachment. As shown at FIG. 7, a Group ID can consist of twoparts namely, a Base Group ID and an Individual ID. The relative size ofeach of these components is determined by a Group Mask. For example, asshown in FIG. 7 (and assuming conventional IPv4 like notation tosimplify exposition), a base group ID and group mask of respectively10.1.0.0 and /16 means a base group ID of 16 bits, leaving 16 bits forthe individual IDs. Base group IDs can be thought of as an identifierfor the application, and the individual IDs as the device identifiersthe application sends to and receives data from.

To address a group of devices, the MME uses a Paging Prefix consistingof a group ID and a mask. A device compares its full group ID to thepaging prefix to determine whether the device is to wake up. Forexample, as shown in FIG. 7, a paging prefix using the group mask, e.g.,10.1.0.0/16, will activate all UEs in the group. On the other extreme,using an Individual UE Mask of /32 a specific UE within the group can beawakened. Using a Chunk Mask (or sub-group mask) in between theseextremes, allows the MME to address subsets of UEs along “prefix-like”boundaries. Once assigned, the group identifiers of the individual UEswithin a base group remain the same throughout, and the prefix basedapproach allows the MME to dynamically address different subgroups(chunks) within the same base group without explicitly informing the UEseach time the subgroups are updated.

With regard to the simulation framework, we model the LTE eNodeB pagingprocedure and the first two steps (preamble transmissions and randomaccess responses) of the RACH procedure as described supra. Thesubsequent messages for connection establishment are sent over dedicated(non-shared) resources, and we therefore do not model them for ourevaluation. We implement the dynamic group adaptation (automatic chunksize selection) and the adaptive RACH algorithms.

The paging slot for a UE device is given by a system frame number (SFN)and a paging occasion (PO) which is calculated based on its identifier(UEID), DRX cycle (T), and a network specific parameter nB, whichspecifies how many paging occasions (POs) or slots are available persystem frame. The number of UEs that can be paged in a slot is given bythe paging slot capacity (P_(s)) of the eNodeB. After receiving a pagingmessage, the UE starts connection setup using the RACH procedure byselecting a preamble from a set of available (network specific) randomaccess preambles and sends the same to the eNodeB in the next randomaccess slot. In case more than two UEs select the same random accesspreamble in the same slot, we randomly choose one of the UE as thecontention winner. The eNodeB responds with a random access response(RAR) message which should reach the UE within a network configured timewindow W_(RAR), otherwise the UE considers the previous step failed andrestarts the RACH procedure. The network is also limited in the numberof identifiers (preamble reception acknowledgement) it can send in asingle RAR (N_(RAR)) due to radio resource constraints. This implies thenetwork can only reply to W_(RAR)*N_(RAR) (N_(RA) ^(max) in Equation 1)UEs per random access slot. UEs that either collide in preambletransmission or do not receive a RAR, restart the random accessprocedure after a random backoff indicator (BI) time as indicated by thenetwork.

TABLE I Simulation Parameters Parameter Setting Random Access SlotPeriod 5 ms Total Number of Preambles 54 Max. Number of PreambleTransmissions 10 Number of UL Grants per RAR (NRAR) 3Ra-ResponseWindowSize (WRAR) 5 ms Backoff Indicator (BI) 60 Paging DRXCycle (T) 640 ms  Paging Capacity per Slot (Ps) 16 nB 1 Max. PagingRetry Limit 2

Table I presents our simulator parameters. We choose typical networkconfiguration values to evaluate our work. In some cases, our parameterchoice (e.g., P_(s) & nB) is also driven by the fact that we want toinvestigate whether M2M communication pattern poses a challenge to thefundamental LTE radio access capacity. For example, we choose 640 ms aspaging DRX cycle which strikes a good balance between paging slot waittimes and frequent device wakeups. Again, we choose the random backoffindicator value (BI=60) to keep the activation delay within a tolerablelimit.

For M2M arrivals we simulate the arrival of 30,000 activation requestsuniformly spread over an interval of 10 s. We consider this demandinguse case in our evaluation to show that our adaptive algorithms scaleefficiently even with extreme workloads.

For H2H (background traffic) we use data obtained from a large cellularservice provider in the US. The purpose of using this data is to showhow the projected M2M traffic patterns affect the existing H2Hcommunications. The data sample consists of one hour of LTE controlplane data. This data, anonymized and aggregated over one second,consists of all GTPC-v2 control plane message exchanged between allMME-SGW pairs. We obtained a time series for both paging (Downlink DataNotification GTPC-v2 message) and new session requests (Create SessionRequest GTPC-v2 message) from this data. The data we obtained wascollected during a relatively quiet network period. Therefore, as weexplain below, we use a scaled version of the data in our simulations.Table II shows the essential statistics for both the one hour datasample and the scaled data. The table shows statistics associated with arepresentative eNB we use in our simulations. The “Data Sample” columnshows the statistics derived from these time series. To scale the datato more realistic rates, we use the well known diurnal pattern in termsof traffic load, to scale the data by a factor 2, from the low volumehour, to a peak hour.

We note that our data did not contain “connection reestablishment”requests which are sent out when a UE in idle state wakes up because ofapplication initiated activity. To account for these messages in oursimulation we assume that connection reestablishment requests are of thesame magnitude as new data session requests. The statistics of theresulting scaled data that we use in our simulations are shown in the“Scaled Data” column in Table II.

TABLE II Data Statistics Data Sample Scaled Data Loadtype Mean (max)Requests/sec. (max) Paging  4.2 (15)  8.4 (30) New Session Requests 2.32(9) 4.64 (18) Idle UE Conn. Re-estab. — 4.64 (18)

Our scaling of the sample data is conservative along several vectors.First, diurnal patterns often show larger differences (than factor 2)between low and peak times. Second, given predominant H2M use of currentmobile devices, connection reestablishment can be expected to happenmore frequently than new connection establishment. Finally, given thatLTE/EPC deployments are relatively new, the bulk of mobile devices arestill being served on 3G networks. For example, recent hourly averageddata from the same provider shows peak paging and peak RRC connectionrequest rates of respectively 36 and 53 requests per second, indicatingthat our scaling factor is indeed conservative.

The M2M arrivals and the real H2H paging data serves as the input to thepaging module in our simulator. The successfully paged UEs, in turn, areused as input to the RACH module. Additionally, the RACH module alsotakes as input the real H2H new data session requests and connectionre-establishment as these messages also contribute to RACH contention.Each simulation run starts with the arrival of the first of the 30,000M2M device activation requests which arrive uniformly over 10 s, andcontinues till all the M2M devices has completed (or failed to complete)the access (paging and RACH) procedures. Since the real trace is for 1hour (much longer than our simulation period), for each simulation runwe select a random starting point in the trace to avoid any bias in theevaluation. For each set of experiment, we use 20 simulation runs andpresent the average result of these 20 runs.

Results for our evaluation are presented below and various evaluationmetrics associated with those results are summarized in Table III.

TABLE III Evaluation Metrics Metric Description Paging CollisionDescription Ratio of number of paging slots in which paging Probabilitycollision occurs, to the total number of paging slots. (Number ofdevices paged exceeds slot capacity.) RACH Collision Ratio of the numberof random access slots in which two or more Probability devices selectthe same preamble to the total number of random access slots. M2MDifference between the time when the request for device Completionactivation for the first device in a M2M group is received and the Timetime when the entire group (last device in the group) successfullycompletes the connection setup procedure (excluding the devices thatfail). Average Wait Average time a device is to wait from the time ofdevice Time activation request to complete the connection setup.Computed for successful devices only. Paging Failure Ratio of totalnumber of devices that could not be paged after the Rate maximum numberof paging message retransmission attempts, to the total number of pagingrequests. RACH Failure Ratio of total number of devices that could notcomplete the Rate RACH procedure after maximum number of preambleretransmission attempts, to the total number of devices participating inthe RACH procedure.

Turning now to FIGS. 8A-8C, graphic illustrations 800, 810, and 820 aredepicted, respectively. Graphic illustrations 800, 810, and 820 compareRACH failure rates, preamble transmission attempts and other data forthe disclosed adaptive techniques compared to naïve implementations.With regard to initial RACH backoff, illustration 800 shows how ouradaptive initial backoff scheme (shown as GP with IB) dramaticallyreduces RACH failure rates as compared to a naive grouping strategy(shown as naive GP) with no adaptive backoff. Results are for grouparrival of 30,000 devices in 10 s. As discussed, such arrival causesunacceptably high paging failures, whereas a naive grouping strategysimply shifts the bottleneck from the paging mechanisms to the RACHprocedure. Our method helps overcome the RACH bottleneck with efficientdistribution of load. For example, RACH failure rates for M2M devicesreduces to only 0.57% (from 95%) and overall RACH collision rate reducesto 14.83% (from 76.65%) when using our strategy.

Illustration 810 shows how RACH failure rate varies with different grouparrival sizes (with and without our proposed adaptation). We experimentwith group arrival sizes of 500, 1000, 2000, 5000, 10,000 and 30,000,respectively. We see that, the RACH failure rates with a naive groupingstrategy increases significantly as group size is increased. Forexample, the failure rate is close to 40% even for a moderately largegroup arrival of 2000. This indicates that, dividing a very large groupinto moderate sized subgroups and paging the subgroups individually doesnot work well without any adaptation. Our adaptive backoff schemeensures that the RACH failure probability is almost zero for moderatelylarge groups and even for a very large group of arrival 30,000, thefailure rate is only 0.57%.

We present the CDF of random access preamble retransmission attempts atFIG. 8C via illustration 820. If a device fails to complete the RACHprocedure after 10 retransmission attempts (excluding the initialtransmission), it aborts the RACH procedure (e.g., a RACH failure). Witheach retransmission the devices increase the transmission power toimprove the chances of preamble detection at the eNodeB, and largenumber of retransmissions negatively impact device battery consumptionas well as wastes valuable network resources. From illustration 820, wesee that more than 50% of the M2M devices undergo 9 or moreretransmissions to successfully complete the RACH procedure while usinga naive grouping strategy, whereas our scheme enables 90% of the M2Mdevices to complete the RACH procedure using only 5-6 retransmissions.Similarly, the 90th percentile for H2H retransmissions is almost 10 fora naive grouping strategy as compared to only 5 for our adaptive backoffscheme.

Referring now to FIGS. 9A-9C, graphic illustrations 900, 910, and 920are depicted, respectively. Graphic illustrations 900, 910, and 920depict RACH failure rates, paging failure rates, and other data for thedisclosed adaptive techniques compared to naïve implementations.Illustrations 900 and 910 show RACH failure rates & collisionprobabilities and paging failure rates & collision probabilities,respectively, when used with different sizes of chunks (or subgroups) toactivate 30,000 devices that arrive uniformly over a 10 second period.The purpose of these experiments to observe how the selection ofdifferent chunk sizes affects the network performance. Chunk size of 1corresponds to not doing any grouping and paging the devicesindividually, and chunk size of 30,000 corresponds to the case when wewait for all the requests to arrive and page them as a single group.Illustration 900 suggests that using smaller chunk sizes reduces RACHfailure rates and collision probabilities for both M2M and H2H traffic.However, Illustration 910 suggests that if we use chunk sizes less than100 (or equivalently, more than 300 chunks) the paging failure rateincreases significantly (e.g., almost 80% M2M devices fail to completethe paging process for a chunk size of 10). The high failure is becauseof the fact that smaller chunk sizes means more paging messages are tobe sent to activate all the devices, and all these chunks will be pagedin slots determined by a common base group identifier, leading to alarge number of collisions. The high volume of paging messages alsonegatively impact the H2H paging success rate (failure rate increases byalmost 18 times when using chunk sizes of 10 as opposed to chunk sizesof 100).

Illustration 920 shows another tradeoff the network considers whileselecting the proper chunk sizes. We see that, if we use very largechunk size e.g., 15,000 or moderately small chunk size (e.g., 100-1000),M2M average wait time and overall completion time increases. This is dueto the fact that large chunk size leads to more RACH collisions andhence more retransmissions, whereas, smaller chunk sizes leads to morenumber of chunks (chunk size of 100 means 300 chunks for a group arrivalof 30,000), and hence more number of paging messages. The cumulativeeffect of paging delay (paging message can be sent in only one slot perDRX cycle) and any overestimation of additional RACH delay leads tooverall increased wait times and completion times for M2M. As evidentfrom the figure, average wait time for H2H remains mostly unaffectedirrespective of the chunk sizes we use because our RACH adaptationmethods ensure that the M2M traffic use the RACH in a well distributedmanner. Moreover, since all M2M chunks only utilize a single paging slotper DRX cycle, H2H paging traffic do not face much competition from M2Mtraffics, hence the paging delay also remains unaffected. We omitresults for a group arrival of 5000 over 10 s which show similar, butless pronounced, results.

FIG. 10A depicts illustration 1000. Illustration 1000 provides acomparison of RACH failure rates with and without disclosed adaptionmechanisms. Illustration 1000 shows why it is beneficial to adapt theRACH process to ensure that consecutive chunks do not overlap with eachother. We ignore chunk sizes less than 100 due to high paging failurerates. For the smaller chunk sizes in the range of [100-5000], the RACHfailure rates are very high, almost 30% for chunk sizes of 200, 20% forchunk sizes of 500, etc. This is due to the fact that, without anyadaptation, it is likely that one chunk is paged at a time when theother chunk is still accessing the RACH, so RACH access attempts fromthe devices belonging to the newly arrived chunk (albeit initial randombackoff) will collide with access attempts from the previous chunksleading to high RACH failure rate. Furthermore, as a consequence of thepaging mechanism, all the chunks that arrive within a paging DRX cycleare paged at the same time. Smaller chunk sizes also increases thenumber of chunks that arrive within a paging DRX cycle and, hence, thenumber of chunks that start the RACH procedure at the same instant. Wesee that, with our adaptation method the RACH failure rate remains closeto zero irrespective of the chunk sizes used. Thus, we achieve an orderof magnitude improvement as far as RACH failure rates are concerned withour adaptation scheme.

FIG. 10B depicts illustration 1010. Illustration 1010 provides acomparison relative to adaptive chunk sizes. Illustration 1010 shows howour adaptive chunk selection method is able to select optimal operatingpoints for different arrival intensities. Recall, the network initiallyselects a default minimum chunk size that avoids high paging failures.In our experiment we set the default value to 100. We see that, forgroup arrivals of 30,000/10 s, the network converges to the optimalchunk size (3840) within only 100 ms of the arrival start time. Theaccess completion time is 12.2 s, whereas the average wait times for M2Mand H2H are 1.1 s and 0.32 s, respectively. Also, RACH and Pagingfailure rates for both M2M and H2H are zero in this case. We note that,these results are consistent with the optimal operating points weobserve in our experiment with different (fixed) chunk sizes (e.g.,FIGS. 9a, 9b, 9c ). For a less intense group arrival of 5000/10 s, thedefault minimum chunk size proves to be the optimal for the entiresimulation period.

Example Methods Associated with Adaptive Group Mechanisms

FIGS. 11 and 12 illustrate various methodologies in accordance with thedisclosed subject matter. While, for purposes of simplicity ofexplanation, the methodologies are shown and described as a series ofacts, it is to be understood and appreciated that the disclosed subjectmatter is not limited by the order of acts, as some acts may occur indifferent orders and/or concurrently with other acts from that shown anddescribed herein. For example, those skilled in the art will understandand appreciate that a methodology could alternatively be represented asa series of interrelated states or events, such as in a state diagram.Moreover, not all illustrated acts may be required to implement amethodology in accordance with the disclosed subject matter.Additionally, it should be further appreciated that the methodologiesdisclosed hereinafter and throughout this specification are capable ofbeing stored on an article of manufacture to facilitate transporting andtransferring such methodologies to computers.

Turning now to FIG. 11, exemplary method 1100 is depicted. Method 1100can provide for adaptively determining a paging group size. For example,at reference numeral 1102, a device comprising a processor, candetermine network load data representing a utilization parameter fornetwork devices of a communication network associated with the device.In some embodiments the network load data can relate to a load for acontrol plane resource.

At reference numeral 1104, the device can determine group size databased on the network load data, wherein the group size data represents anumber of user equipment devices that are paged as a group. This groupsize can be determined based in order to reduce failure rates and/orconnection times associated with waking the devices as a group.

At reference numeral 1106, the device can create a paging messagecomprising activation data that instructs the user equipment devices toswitch from an idle state to an active state and the group size data. Insome embodiments, the paging message can be transmitted to one or moreaccess point devices such as on or more eNBs to facilitate waking updevices served by those eNBs. Method 1100 can proceed to insert A, whichis further detailed in connection with FIG. 12, or end.

With reference now to FIG. 12, exemplary method 1200 is illustrated.Method 1200 can provide for additional elements or aspects in connectionwith adaptively determining a paging group size. For example, atreference numeral 1202, the device can determine group membership datathat identifies the user equipment devices that are paged as a group.Hence, the device can determine both a size of a paging group as well asthe devices that are to be members of the group.

As described at reference numeral 1204, determining group membershipdata can be performed as a function of type data indicative of a type ofa user equipment device, type size data indicative of a number of userequipment devices of the type, frequency data indicative of an estimatedfrequency the user equipment device is paged, or based on other suitableparameters.

At reference numeral 1206, the device can determine failure rate dataindicative of a proportion of user equipment devices of the userequipment devices that are paged as a group that failed to establish aconnection in response to receipt of the paging message. In someembodiments, the device can further determine completion time datarepresenting a time for a group of RACH procedures to complete. In someembodiments, completion time data can be included in failure rate data.

At reference numeral 1208, the device can update the group size data andthe group membership data based on the failure rate data and the networkload data. It is appreciated that completion rate data can be employedas well to update the groups size data.

Example Operating Environments

To provide further context for various aspects of the subjectspecification, FIG. 13 illustrates an example wireless communicationenvironment 1300, with associated components that can enable operationof a femtocell enterprise network in accordance with aspects describedherein. Wireless communication environment 1300 comprises two wirelessnetwork platforms: (i) A macro network platform 1310 that serves, orfacilitates communication) with user equipment 1375 via a macro radioaccess network (RAN) 1370. It should be appreciated that in cellularwireless technologies (e.g., 4G, 3GPP UMTS, HSPA, 3GPP LTE, 3GPP UMB,5G), macro network platform 1310 is embodied in a Core Network. (ii) Afemto network platform 1380, which can provide communication with UE1375 through a femto RAN 1390, linked to the femto network platform 1380through a routing platform 1310 via backhaul pipe(s) 1385. It should beappreciated that femto network platform 1380 typically offloads UE 1375from macro network, once UE 1375 attaches (e.g., through macro-to-femtohandover, or via a scan of channel resources in idle mode) to femto RAN.

It is noted that RAN comprises base station(s), or access point(s), andits associated electronic circuitry and deployment site(s), in additionto a wireless radio link operated in accordance with the basestation(s). Accordingly, macro RAN 1370 can comprise various coveragecells, while femto RAN 1390 can comprise multiple femto access points ormultiple metro cell access points. As mentioned above, it is to beappreciated that deployment density in femto RAN 1390 can besubstantially higher than in macro RAN 1370.

Generally, both macro and femto network platforms 1310 and 1380 comprisecomponents, e.g., nodes, gateways, interfaces, servers, or platforms,that facilitate both packet-switched (PS) (e.g., internet protocol (IP),Ethernet, frame relay, asynchronous transfer mode (ATM)) andcircuit-switched (CS) traffic (e.g., voice and data) and controlgeneration for networked wireless communication. In an aspect of thesubject innovation, macro network platform 1310 comprises CS gatewaynode(s) 1312 which can interface CS traffic received from legacynetworks like telephony network(s) 1340 (e.g., public switched telephonenetwork (PSTN), or public land mobile network (PLMN)) or a SS7 network1360. Circuit switched gateway 1312 can authorize and authenticatetraffic (e.g., voice) arising from such networks. Additionally, CSgateway 1312 can access mobility, or roaming, data generated through SS7network 1360; for instance, mobility data stored in a VLR, which canreside in memory 1330. Moreover, CS gateway node(s) 1312 interfacesCS-based traffic and signaling and gateway node(s) 1318. As an example,in a 3GPP UMTS network, gateway node(s) 1318 can be embodied in gatewayGPRS support node(s) (GGSN).

In addition to receiving and processing CS-switched traffic andsignaling, gateway node(s) 1318 can authorize and authenticate PS-baseddata sessions with served (e.g., through macro RAN) wireless devices.Data sessions can comprise traffic exchange with networks external tothe macro network platform 1310, like wide area network(s) (WANs) 1350;it should be appreciated that local area network(s) (LANs) can also beinterfaced with macro network platform 1310 through gateway node(s)1318. Gateway node(s) 1318 generates packet data contexts when a datasession is established. To that end, in an aspect, gateway node(s) 1318can comprise a tunnel interface (e.g., tunnel termination gateway (TTG)in 3GPP UMTS network(s); not shown) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks. It should be further appreciated that the packetizedcommunication can comprise multiple flows that can be generated throughserver(s) 1314. It is to be noted that in 3GPP UMTS network(s), gatewaynode(s) 1318 (e.g., GGSN) and tunnel interface (e.g., TTG) comprise apacket data gateway (PDG).

Macro network platform 1310 also comprises serving node(s) 1316 thatconvey the various packetized flows of information or data streams,received through gateway node(s) 1318. As an example, in a 3GPP UMTSnetwork, serving node(s) can be embodied in serving GPRS support node(s)(SGSN).

As indicated above, server(s) 1314 in macro network platform 1310 canexecute numerous applications (e.g., location services, online gaming,wireless banking, wireless device management . . . ) that generatemultiple disparate packetized data streams or flows, and manage (e.g.,schedule, queue, format . . . ) such flows. Such application(s), forexample can comprise add-on features to standard services provided bymacro network platform 1310. Data streams can be conveyed to gatewaynode(s) 1318 for authorization/authentication and initiation of a datasession, and to serving node(s) 1316 for communication thereafter.Server(s) 1314 can also effect security (e.g., implement one or morefirewalls) of macro network platform 1310 to ensure network's operationand data integrity in addition to authorization and authenticationprocedures that CS gateway node(s) 1312 and gateway node(s) 1318 canenact. Moreover, server(s) 1314 can provision services from externalnetwork(s), e.g., WAN 1350, or Global Positioning System (GPS)network(s) (not shown). It is to be noted that server(s) 1314 cancomprise one or more processor configured to confer at least in part thefunctionality of macro network platform 1310. To that end, the one ormore processor can execute code instructions stored in memory 1330, forexample.

In example wireless environment 1300, memory 1330 stores informationrelated to operation of macro network platform 1310. Information cancomprise business data associated with subscribers; market plans andstrategies, e.g., promotional campaigns, business partnerships;operational data for mobile devices served through macro networkplatform; service and privacy policies; end-user service logs for lawenforcement; and so forth. Memory 1330 can also store information fromat least one of telephony network(s) 1340, WAN(s) 1350, or SS7 network1360, enterprise NW(s) 1365, or service NW(s) 1367.

Femto gateway node(s) 1384 have substantially the same functionality asPS gateway node(s) 1318. Additionally, femto gateway node(s) 1384 canalso comprise substantially all functionality of serving node(s) 1316.In an aspect, femto gateway node(s) 1384 facilitates handoverresolution, e.g., assessment and execution. Further, control node(s)1320 can receive handover requests and relay them to a handovercomponent (not shown) via gateway node(s) 1384. According to an aspect,control node(s) 1320 can support RNC capabilities.

Server(s) 1382 have substantially the same functionality as described inconnection with server(s) 1314. In an aspect, server(s) 1382 can executemultiple application(s) that provide service (e.g., voice and data) towireless devices served through femto RAN 1390. Server(s) 1382 can alsoprovide security features to femto network platform. In addition,server(s) 1382 can manage (e.g., schedule, queue, format . . . )substantially all packetized flows (e.g., IP-based) it generates inaddition to data received from macro network platform 1310. It is to benoted that server(s) 1382 can comprise one or more processor configuredto confer at least in part the functionality of macro network platform1310. To that end, the one or more processor can execute codeinstructions stored in memory 1386, for example.

Memory 1386 can comprise information relevant to operation of thevarious components of femto network platform 1380. For exampleoperational information that can be stored in memory 1386 can comprise,but is not limited to, subscriber information; contracted services;maintenance and service records; femto cell configuration (e.g., devicesserved through femto RAN 1390; access control lists, or white lists);service policies and specifications; privacy policies; add-on features;and so forth.

It is noted that femto network platform 1380 and macro network platform1310 can be functionally connected through one or more reference link(s)or reference interface(s). In addition, femto network platform 1380 canbe functionally coupled directly (not illustrated) to one or more ofexternal network(s) 1340, 1350, 1360, 1365 or 1367. Reference link(s) orinterface(s) can functionally link at least one of gateway node(s) 1384or server(s) 1386 to the one or more external networks 1340, 1350, 1360,1365 or 1367.

FIG. 14 illustrates a wireless environment that comprises macro cellsand femtocells for wireless coverage in accordance with aspectsdescribed herein. In wireless environment 1405, two areas represent“macro” cell coverage; each macro cell is served by a base station 1410.It can be appreciated that macro cell coverage area 1405 and basestation 1410 can comprise functionality, as more fully described herein,for example, with regard to system 1400. Macro coverage is generallyintended to serve mobile wireless devices, like UE 1420 _(A), 1420 _(B),in outdoors locations. An over-the-air (OTA) wireless link 1435 providessuch coverage, the wireless link 1435 comprises a downlink (DL) and anuplink (UL), and utilizes a predetermined band, licensed or unlicensed,of the radio frequency (RF) spectrum. As an example, UE 1420 _(A), 1420_(E) can be a 3GPP Universal Mobile Telecommunication System (UMTS)mobile phone. It is noted that a set of base stations, its associatedelectronics, circuitry or components, base stations controlcomponent(s), and wireless links operated in accordance to respectivebase stations in the set of base stations form a radio access network(RAN). In addition, base station 1410 communicates via backhaul link(s)1451 with a macro network platform 1460, which in cellular wirelesstechnologies (e.g., 3rd Generation Partnership Project (3GPP) UniversalMobile Telecommunication System (UMTS), Global System for MobileCommunication (GSM)) represents a core network.

In an aspect, macro network platform 1460 controls a set of basestations 1410 that serve either respective cells or a number of sectorswithin such cells. Base station 1410 comprises radio equipment 1414 foroperation in one or more radio technologies, and a set of antennas 1412(e.g., smart antennas, microwave antennas, satellite dish(es) . . . )that can serve one or more sectors within a macro cell 1405. It is notedthat a set of radio network control node(s), which can be a part ofmacro network platform 1460; a set of base stations (e.g., Node B 1410)that serve a set of macro cells 1405; electronics, circuitry orcomponents associated with the base stations in the set of basestations; a set of respective OTA wireless links (e.g., links 1415 or1416) operated in accordance to a radio technology through the basestations; and backhaul link(s) 1455 and 1451 form a macro radio accessnetwork (RAN). Macro network platform 1460 also communicates with otherbase stations (not shown) that serve other cells (not shown). Backhaullink(s) 1451 or 1453 can comprise a wired backbone link (e.g., opticalfiber backbone, twisted-pair line, T1/E1 phone line, a digitalsubscriber line (DSL) either synchronous or asynchronous, an asymmetricADSL, or a coaxial cable . . . ) or a wireless (e.g., line-of-sight(LOS) or non-LOS) backbone link. Backhaul pipe(s) 1455 link disparatebase stations 1410. According to an aspect, backhaul link 1453 canconnect multiple femto access points 1430 and/or controller components(CC) 1401 to the femto network platform 1402. In one example, multiplefemto APs can be connected to a routing platform (RP) 1487, which inturn can be connect to a controller component (CC) 1401. Typically, theinformation from UEs 1420 _(A) can be routed by the RP 1487, forexample, internally, to another UE 1420 _(A) connected to a disparatefemto AP connected to the RP 1487, or, externally, to the femto networkplatform 1402 via the CC 1401, as discussed in detail supra.

In wireless environment 1405, within one or more macro cell(s) 1405, aset of femtocells 1445 served by respective femto access points (APs)1430 can be deployed. It can be appreciated that, aspects of the subjectinnovation can be geared to femtocell deployments with substantive femtoAP density, e.g., 14⁴-10⁷ femto APs 1430 per base station 1410.According to an aspect, a set of femto access points 1430 ₁-1430 _(N),with N a natural number, can be functionally connected to a routingplatform 1487, which can be functionally coupled to a controllercomponent 1401. The controller component 1401 can be operationallylinked to the femto network platform 1402 by employing backhaul link(s)1453. Accordingly, UE 1420 _(A) connected to femto APs 1430 ₁-1430 _(N)can communicate internally within the femto enterprise via the routingplatform (RP) 1487 and/or can also communicate with the femto networkplatform 1402 via the RP 1487, controller component 1401 and thebackhaul link(s) 1453. It can be appreciated that although only onefemto enterprise is depicted in FIG. 14, multiple femto enterprisenetworks can be deployed within a macro cell 1405.

It is noted that while various aspects, features, or advantagesdescribed herein have been illustrated through femto access point(s) andassociated femto coverage, such aspects and features also can beexploited for home access point(s) (HAPs) that provide wireless coveragethrough substantially any, or any, disparate telecommunicationtechnologies, such as for example Wi-Fi (wireless fidelity) or picocelltelecommunication. Additionally, aspects, features, or advantages of thesubject innovation can be exploited in substantially any wirelesstelecommunication, or radio, technology; for example, Wi-Fi, WorldwideInteroperability for Microwave Access (WiMAX), Enhanced General PacketRadio Service (Enhanced GPRS), 3GPP LTE, 3GPP2 UMB, 3GPP UMTS, HSPA,HSDPA, HSUPA, or LTE Advanced. Moreover, substantially all aspects ofthe subject innovation can comprise legacy telecommunicationtechnologies.

With respect to FIG. 14, in example embodiment 1400, base station AP1410 can receive and transmit signal(s) (e.g., traffic and controlsignals) from and to wireless devices, access terminals, wireless portsand routers, etc., through a set of antennas 1412 ₁-1412 _(N). It shouldbe appreciated that while antennas 1412 ₁-1412 _(N) are a part ofcommunication platform 1425, which comprises electronic components andassociated circuitry that provides for processing and manipulating ofreceived signal(s) (e.g., a packet flow) and signal(s) (e.g., abroadcast control channel) to be transmitted. In an aspect,communication platform 1425 comprises a transmitter/receiver (e.g., atransceiver) 1466 that can convert signal(s) from analog format todigital format upon reception, and from digital format to analog formatupon transmission. In addition, receiver/transmitter 1466 can divide asingle data stream into multiple, parallel data streams, or perform thereciprocal operation. Coupled to transceiver 1466 is amultiplexer/demultiplexer 1467 that facilitates manipulation of signalin time and frequency space. Electronic component 1467 can multiplexinformation (data/traffic and control/signaling) according to variousmultiplexing schemes such as time division multiplexing (TDM), frequencydivision multiplexing (FDM), orthogonal frequency division multiplexing(OFDM), code division multiplexing (CDM), space division multiplexing(SDM). In addition, mux/demux component 1467 can scramble and spreadinformation (e.g., codes) according to substantially any code known inthe art; e.g., Hadamard-Walsh codes, Baker codes, Kasami codes,polyphase codes, and so on. A modulator/demodulator 1468 is also a partof operational group 1425, and can modulate information according tomultiple modulation techniques, such as frequency modulation, amplitudemodulation (e.g., M-ary quadrature amplitude modulation (QAM), with M apositive integer), phase-shift keying (PSK), and the like.

Referring now to FIG. 15, there is illustrated a block diagram of anexemplary computer system operable to execute the disclosedarchitecture. In order to provide additional context for various aspectsof the disclosed subject matter, FIG. 15 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 1500 in which the various aspects of the disclosedsubject matter can be implemented. Additionally, while the disclosedsubject matter described above may be suitable for application in thegeneral context of computer-executable instructions that may run on oneor more computers, those skilled in the art will recognize that thedisclosed subject matter also can be implemented in combination withother program modules and/or as a combination of hardware and software.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the disclosed subject matter may also bepracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

A computer typically comprises a variety of computer-readable media.Computer-readable media can be any available media that can be accessedby the computer and comprises both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer-readable media can comprise computer storage mediaand communication media. Computer storage media can comprise eithervolatile or nonvolatile, removable and non-removable media implementedin any method or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media comprises, but is not limited to,RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM,digital versatile disk (DVD) or other optical disk storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the computer.

Communication media typically embodies computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism, andcomprises any information delivery media. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a manner as to encode information in the signal. Byway of example, and not limitation, communication media comprises wiredmedia such as a wired network or direct-wired connection, and wirelessmedia such as acoustic, RF, infrared and other wireless media.Combinations of the any of the above should also be included within thescope of computer-readable media.

Still referring to FIG. 15, the exemplary environment 1500 forimplementing various aspects of the disclosed subject matter comprises acomputer 1502, the computer 1502 including a processing unit 1504, asystem memory 1506 and a system bus 1508. The system bus 1508 couples tosystem components including, but not limited to, the system memory 1506to the processing unit 1504. The processing unit 1504 can be any ofvarious commercially available processors. Dual microprocessors andother multi-processor architectures may also be employed as theprocessing unit 1504.

The system bus 1508 can be any of several types of bus structure thatmay further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1506comprises read-only memory (ROM) 1510 and random access memory (RAM)1512. A basic input/output system (BIOS) is stored in a non-volatilememory 1510 such as ROM, EPROM, EEPROM, which BIOS contains the basicroutines that help to transfer information between elements within thecomputer 1502, such as during start-up. The RAM 1512 can also comprise ahigh-speed RAM such as static RAM for caching data.

The computer 1502 further comprises an internal hard disk drive (HDD)1514 (e.g., EIDE, SATA), which internal hard disk drive 1514 may also beconfigured for external use in a suitable chassis (not shown), amagnetic floppy disk drive (FDD) 1516, (e.g., to read from or write to aremovable diskette 1518) and an optical disk drive 1520, (e.g., readinga CD-ROM disk 1522 or, to read from or write to other high capacityoptical media such as the DVD). The hard disk drive 1514, magnetic diskdrive 1516 and optical disk drive 1520 can be connected to the systembus 1508 by a hard disk drive interface 1524, a magnetic disk driveinterface 1526 and an optical drive interface 1528, respectively. Theinterface 1524 for external drive implementations comprises at least oneor both of Universal Serial Bus (USB) and IEEE1394 interfacetechnologies. Other external drive connection technologies are withincontemplation of the subject matter disclosed herein.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1502, the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer, such as zipdrives, magnetic cassettes, flash memory cards, cartridges, and thelike, may also be used in the exemplary operating environment, andfurther, that any such media may contain computer-executableinstructions for performing the methods of the disclosed subject matter.

A number of program modules can be stored in the drives and RAM 1512,including an operating system 1530, one or more application programs1532, other program modules 1534 and program data 1536. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1512. It is appreciated that the disclosed subjectmatter can be implemented with various commercially available operatingsystems or combinations of operating systems.

A user can enter commands and information into the computer 1502 throughone or more wired/wireless input devices, e.g., a keyboard 1538 and apointing device, such as a mouse 1540. Other input devices (not shown)may comprise a microphone, an IR remote control, a joystick, a game pad,a stylus pen, touch screen, or the like. These and other input devicesare often connected to the processing unit 1504 through an input deviceinterface 1542 that is coupled to the system bus 1508, but can beconnected by other interfaces, such as a parallel port, an IEEE1394serial port, a game port, a USB port, an IR interface, etc.

A monitor 1544 or other type of display device is also connected to thesystem bus 1508 via an interface, such as a video adapter 1546. Inaddition to the monitor 1544, a computer typically comprises otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1502 may operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1548. The remotecomputer(s) 1548 can be a workstation, a server computer, a router, apersonal computer, a mobile device, portable computer,microprocessor-based entertainment appliance, a peer device or othercommon network node, and typically comprises many or all of the elementsdescribed relative to the computer 1502, although, for purposes ofbrevity, only a memory/storage device 1550 is illustrated. The logicalconnections depicted comprise wired/wireless connectivity to a localarea network (LAN) 1552 and/or larger networks, e.g., a wide areanetwork (WAN) 1554. Such LAN and WAN networking environments arecommonplace in offices and companies, and facilitate enterprise-widecomputer networks, such as intranets, all of which may connect to aglobal communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 1502 isconnected to the local network 1552 through a wired and/or wirelesscommunication network interface or adapter 1556. The adapter 1556 mayfacilitate wired or wireless communication to the LAN 1552, which mayalso comprise a wireless access point disposed thereon for communicatingwith the wireless adapter 1556.

When used in a WAN networking environment, the computer 1502 cancomprise a modem 1558, or is connected to a communications server on theWAN 1554, or has other means for establishing communications over theWAN 1554, such as by way of the Internet. The modem 1558, which can beinternal or external and a wired or wireless device, is connected to thesystem bus 1508 via the serial port interface 1542. In a networkedenvironment, program modules depicted relative to the computer 1502, orportions thereof, can be stored in the remote memory/storage device1550. It will be appreciated that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers can be used.

The computer 1502 is operable to communicate with any wireless devicesor entities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,restroom), and telephone. This comprises at least Wi-Fi and Bluetooth™wireless technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from acouch at home, a bed in a hotel room, or a conference room at work,without wires. Wi-Fi is a wireless technology similar to that used in acell phone that enables such devices, e.g., computers, to send andreceive data indoors and out; anywhere within the range of a basestation. Wi-Fi networks use radio technologies called IEEE802.11 (a, b,g, n, etc.) to provide secure, reliable, fast wireless connectivity. AWi-Fi network can be used to connect computers to each other, to theInternet, and to wired networks (which use IEEE802.3 or Ethernet). Wi-Finetworks operate in the unlicensed 2.4 and 5 GHz radio bands, at an 11Mbps (802.11b) or 54 Mbps (802.11a) data rate, for example, or withproducts that contain both bands (dual band), so the networks canprovide real-world performance similar to the basic “10BaseT” wiredEthernet networks used in many offices.

What has been described above comprises examples of the variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the embodiments, but one of ordinary skill in the art mayrecognize that many further combinations and permutations are possible.Accordingly, the detailed description is intended to embrace all suchalterations, modifications, and variations that fall within the spiritand scope of the appended claims.

As used in this application, the terms “system,” “component,”“interface,” and the like are generally intended to refer to acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component may be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. These components also can execute from various computerreadable storage media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry that is operated bysoftware or firmware application(s) executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. An interface can comprise input/output (I/O)components as well as associated processor, application, and/or APIcomponents.

Furthermore, the disclosed subject matter may be implemented as amethod, apparatus, or article of manufacture using standard programmingand/or engineering techniques to produce software, firmware, hardware,or any combination thereof to control a computer to implement thedisclosed subject matter. The term “article of manufacture” as usedherein is intended to encompass a computer program accessible from by acomputing device.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Processors can exploit nano-scale architectures suchas, but not limited to, molecular and quantum-dot based transistors,switches and gates, in order to optimize space usage or enhanceperformance of user equipment. A processor also can be implemented as acombination of computing processing units.

In the subject specification, terms such as “store,” “data store,” “datastorage,” “database,” “repository,” “queue”, and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory. In addition, memory components or memory elementscan be removable or stationary. Moreover, memory can be internal orexternal to a device or component, or removable or stationary. Memorycan comprise various types of media that are readable by a computer,such as hard-disc drives, zip drives, magnetic cassettes, flash memorycards or other types of memory cards, cartridges, or the like.

By way of illustration, and not limitation, nonvolatile memory cancomprise read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable ROM (EEPROM), or flashmemory. Volatile memory can comprise random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), anddirect Rambus RAM (DRRAM). Additionally, the disclosed memory componentsof systems or methods herein are intended to comprise, without beinglimited to comprising, these and any other suitable types of memory.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms (including a reference to a “means”) used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., a functional equivalent), even though not structurallyequivalent to the disclosed structure, which performs the function inthe herein illustrated exemplary aspects of the embodiments. In thisregard, it will also be recognized that the embodiments comprises asystem as well as a computer-readable medium having computer-executableinstructions for performing the acts and/or events of the variousmethods.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media cancomprise, but are not limited to, RAM, ROM, EEPROM, flash memory orother memory technology, CD-ROM, digital versatile disk (DVD) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or other tangible and/ornon-transitory media which can be used to store desired information.Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

On the other hand, communications media typically embodycomputer-readable instructions, data structures, program modules orother structured or unstructured data in a data signal such as amodulated data signal, e.g., a carrier wave or other transportmechanism, and comprises any information delivery or transport media.The term “modulated data signal” or signals refers to a signal that hasone or more of its characteristics set or changed in such a manner as toencode information in one or more signals. By way of example, and notlimitation, communications media comprise wired media, such as a wirednetwork or direct-wired connection, and wireless media such as acoustic,RF, infrared and other wireless media

Further, terms like “user equipment,” “user device,” “mobile device,”“mobile,” station,” “access terminal,” “terminal,” “handset,” andsimilar terminology, generally refer to a wireless device utilized by asubscriber or user of a wireless communication network or service toreceive or convey data, control, voice, video, sound, gaming, orsubstantially any data-stream or signaling-stream. The foregoing termsare utilized interchangeably in the subject specification and relateddrawings. Likewise, the terms “access point,” “node B,” “base station,”“evolved Node B,” “cell,” “cell site,” and the like, can be utilizedinterchangeably in the subject application, and refer to a wirelessnetwork component or appliance that serves and receives data, control,voice, video, sound, gaming, or substantially any data-stream orsignaling-stream from a set of subscriber stations. Data and signalingstreams can be packetized or frame-based flows. It is noted that in thesubject specification and drawings, context or explicit distinctionprovides differentiation with respect to access points or base stationsthat serve and receive data from a mobile device in an outdoorenvironment, and access points or base stations that operate in aconfined, primarily indoor environment overlaid in an outdoor coveragearea. Data and signaling streams can be packetized or frame-based flows.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,” andthe like are employed interchangeably throughout the subjectspecification, unless context warrants particular distinction(s) amongthe terms. It should be appreciated that such terms can refer to humanentities, associated devices, or automated components supported throughartificial intelligence (e.g., a capacity to make inference based oncomplex mathematical formalisms) which can provide simulated vision,sound recognition and so forth. In addition, the terms “wirelessnetwork” and “network” are used interchangeable in the subjectapplication, when context wherein the term is utilized warrantsdistinction for clarity purposes such distinction is made explicit.

Moreover, the word “exemplary” is used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the wordexemplary is intended to present concepts in a concrete fashion. As usedin this application, the term “or” is intended to mean an inclusive “or”rather than an exclusive “or”. That is, unless specified otherwise, orclear from context, “X employs A or B” is intended to mean any of thenatural inclusive permutations. That is, if X employs A; X employs B; orX employs both A and B, then “X employs A or B” is satisfied under anyof the foregoing instances. In addition, the articles “a” and “an” asused in this application and the appended claims should generally beconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form.

In addition, while a particular feature may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.Furthermore, to the extent that the terms “includes” and “including” andvariants thereof are used in either the detailed description or theclaims, these terms are intended to be inclusive in a manner similar tothe term “comprising.”

What is claimed is:
 1. A device, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: determiningnetwork load data representing utilization of a network resource fornetwork devices of a communication network associated with the device;generating a paging message data structure comprising a first datastructure and a second data structure, wherein the first data structureis associated with activation data that instructs a group of userequipment devices to switch from an idle state to an active state andthe second data structure is associated with group size data indicativeof a size of the group of user equipment devices; determining failurerate data indicative of a proportion of user equipment devices of thegroup that failed to establish a connection; determining the group sizedata as a function of the network load data and the failure rate data;and storing the group size data in the paging message data structure. 2.The device of claim 1, wherein the device is a mobility managemententity device associated with the communication network that managesidle mode paging of the group of user equipment devices.
 3. The deviceof claim 1, wherein the network load data represents utilization of acontrol plane resource associated with the communication network.
 4. Thedevice of claim 1, wherein the operations further comprise determiningmembership of the group of user equipment devices based on definedcharacteristic data associated with potential members of the group. 5.The device of claim 4, wherein the defined characteristic data comprisesat least one of: type data indicative of a type of a user equipmentdevice, type size data indicative of a number of user equipment devicesof the type in the group, an indication of location of the userequipment device, or frequency data indicative of an estimated frequencythe user equipment device is paged.
 6. The device of claim 1, whereinthe operations further comprise transmitting the paging message datastructure to an evolved Node B device that is identified by a trackingarea data structure associated with a user equipment device of the groupof user equipment devices.
 7. The device of claim 6, wherein theoperations further comprise receiving from the evolved Node B deviceconnection setup requests associated with the group of user equipmentdevices and determining the failure rate data in response to thetransmitting the paging message data structure.
 8. The device of claim7, wherein the determining the group size data as a function of thefailure rate data is determined to reduce the proportion of the userequipment devices of the group that failed to establish the connection.9. A device, comprising: a processor; and a memory that storesexecutable instructions that, when executed by the processor, facilitateperformance of operations, comprising: receiving a paging message datastructure comprising a first data structure and a second data structure,wherein the first data structure comprises activation data thatinstructs a group of user equipment devices to switch from an idle stateto an active state and the second data structure comprises group sizedata indicative of a size of the group of user equipment devices;determining capacity data indicative of a first number of user equipmentdevice connections supported by the device; determining random accesschannel backoff data as a function of a ratio of the group size data andthe capacity data; updating the random access channel backoff data as afunction of random access channel completion rates of random accesschannel procedures performed by the group of user equipment devices; andfacilitating transmission of a paging message comprising the activationdata and the random access channel backoff data to the group of userequipment devices.
 10. The device of claim 9, wherein the device is anevolved Node B device associated with the communication network thatoperates as an access point device for the group of user equipmentdevices.
 11. The device of claim 9, wherein the determining the capacitydata comprises determining the capacity data as a function of a secondnumber of user equipment device connections that exist for the device.12. The device of claim 9, wherein the random access channel backoffdata represents a time window during which user equipment devices of thegroup are to initiate a random access channel procedure to attach to thedevice.
 13. The device of claim 12, wherein the random access channelbackoff data comprises an instruction for the user equipment devices ofthe group to delay initiation of the random access channel procedure bya random amount of time that ranges from zero to a maximum value of thetime window.
 14. The device of claim 9, wherein the operations furthercomprise determining a non-overlap delay parameter as a function ofrandom access channel completion times associated with random accesschannel procedures by the group of user equipment devices.
 15. Thedevice of claim 9, wherein the operations further comprise updating timewindow data indicative of temporal bounds of a delay described by therandom access channel backoff data as a function of the capacity data.16. A method, comprising: determining, by a device comprising aprocessor, network load data representing a utilization parameter fornetwork devices of a communication network associated with the device;determining, by the device, failure rate data indicative of a proportionof user equipment devices of that are paged together as a group thatfailed to establish a connection; determining, by the device, group sizedata based on the network load data and the failure rate data, whereinthe group size data represents a first number of user equipment devicesthat are paged as the group; and creating, by the device, a pagingmessage comprising activation data that instructs the user equipmentdevices to switch from an idle state to an active state and the groupsize data.
 17. The method of claim 16, further comprising determining,by the device, group membership data that identifies the user equipmentdevices that are paged as the group.
 18. The method of claim 17, whereinthe determining the group membership data comprises determining thegroup membership data as a function of type data indicative of a type ofa user equipment device of the user equipment devices, type size dataindicative of number of user equipment devices of the type, or frequencydata indicative of an estimated frequency by which the user equipmentdevice is paged.
 19. The method of claim 16, further comprisingdetermining, by the device, capacity data indicative of a number of userequipment device connections supported by an access point device. 20.The method of claim 19, further comprising determining, by the device,random access channel backoff data as a function of a ratio of the groupsize data and the capacity data.